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Mobility and COVID-19 in Andorra

Country-scale analysis of high-resolution mobility patterns and infection spread

Spatial variation of “Interaction Potential” over the course of two days. Left: March 12th, just before introduction of lockdown. Right: March 19th, just after introduction of lockdown. Potential interactions are binned into H3 cells of resolution 11 (Uber, 2020). For privacy reasons, only cells with at least 100 potential interactions are shown — credit Ronan Doorley.

By Ronan Doorley, Alex Berke, Ariel Noyman, Luis Alonso, J Ribo, V Arroyo, Marc Pons and Kent Larson
from MIT City Science and the City Science Lab @ Andorra

The MIT Media Lab City Science group and the Andorra Innovation Hub have been collaborating since 2015 on research related to urban programming, new mobility systems, tourism, energy, sustainability and other urban innovations. In 2020, in response to the COVID-19 pandemic, the focus of our partnership shifted towards developing models and analysis for better understanding the relationship between government policies, mobility behavior and the spread of COVID-19.

All over the world, non-pharmaceutical interventions, such as mobility restrictions, have been globally adopted as critically important strategies for curbing the spread of COVID-19. However, such interventions come with immense social and economic costs and the relative effectiveness of different mobility restrictions are not well understood.

In May 2020, the Andorran government carried out a voluntary population-wide serological screening for COVID-19 antibodies (Royo-Cebrecos et al., 2020). We were provided access to the resulting serology data through our collaboration with the Andorra Innovation Hub. We also have access to geolocated telecoms data for all mobile subscribers in Andorra, providing a rich source of information about how people are moving around the country. The combination of serological data and telecoms data presented a unique opportunity to better understand the spread of COVID-19 and its relationship to the mobility of the population.

The aim of this work was to analyze the changes in mobility behaviors and interactions between March and October, 2020, and to test if they were correlated with changes in transmission rate.

Methods

Data

We analyzed three sources of data:

1. Serology

In May 2020, the Andorran government carried out a voluntary serological screening for both IgG and IgM antibodies of COVID-19, in which 91% of the population participated (Royo-Cebrecos et al., 2020). The data were used to estimate the total number of people had been infected in each parish.

2. Spatio-temporal telecom data

Telecom data was provided by Andorra Telecom (AT). Since AT is the sole telecom provider in Andorra, the data covers all mobile subscribers in the country, unlike most telecom datasets where the market is fragmented. Each observation in the AT data includes a unique ID for the subscriber, a timestamp, the coordinates of the device at the time of the update, and nationality for the subscriber’s home network.

The telecom mesh in Andorra La Vella. Data collected in these towers was used to localize tens of thousands of locals and visitors every second, over a period of several months. Overall, millions of data points uncovered patterns of urban behavior at the city-scale. — credit: Ariel Noyman

3. COVID-19 case reports

We used the official COVID-19 case reports for the country of Andorra, from the Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (Dong & Gardner, 2020).

Time series of COVID-19 case reports in Andorra — credit: Alex Berke

Analysis

We computed a range of mobility metrics across the population on a daily basis between the beginning of March 2020 and the end of October 2020. These included variations of the following metrics:

  • The numbers of trips being made, within each parish and between parishes.
  • The numbers of people staying home each day
  • The interaction potential: an aggregation of all the time spent by any pair of individuals in close proximity to one another (~50m).

The time series of each mobility metric was compared to a proxy for transmission rate: the log growth in cases over 2 week intervals, normalized by the susceptible fraction of the population. The comparison was done over a range of lag times from -30 days (mobility behavior leads infection rate by 30 days) to +30 days (mobility lags infection rate by 30 days).

Results and Discussion

As shown in the figure below, the various mobility metrics calculated showed broadly similar trends, dropping off sharply during March and remaining close to their minimum levels throughout April.

The time series for 8 mobility metrics are plotted. The time series for the estimated log case growth (LCG), shifted by a lag of -18 days is superimposed on the mobility metrics plots to better display the correlations between mobility and transmission rates — credit: Alex Berke

The most dramatic change in mobility behaviors came immediately after the introduction of the lockdown over the weekend of March 14th and 15th (see figure). The metrics also show a gradual increase in mobility during May, corresponding with a gradual relaxation of policies, or adherence to those policies.

Spatial variation of “Interaction Potential” over the course of two days. Left: March 12th, just before introduction of lockdown. Right: March 19th, just after introduction of lockdown. Potential interactions are binned into H3 cells of resolution 11 (Uber, 2020). For privacy reasons, only cells with at least 100 potential interactions are shown. Credit: Ronan Doorley

We found that some of the mobility metrics we computed had more substantial correlations with transmission rate than others. As shown in the figure below, the strength and direction of the correlations depended on the lag period. Four metrics (Indoor interaction potential, outdoor interaction potential, number of trips between parishes and number of people making trips between parishes) were highly correlated with transmission, leading by 18–21 days. This suggests that when the behaviors associated with these mobility metrics change, transmission rates respond about 18–21 days later. Three metrics (portion of people staying home, number of people staying home and total trips) were positively correlated with transmission with a lead of 2–3 weeks, and also moderately negatively correlated with transmission rate, with a lag of about 2–3 weeks. This suggests that (i) when these mobility behaviors increase, there is an increase in transmission rate 2–3 weeks later and (ii) when the transmission rate increases, there is a decrease in the mobility behaviors 2–3 weeks later.

Correlations between mobility metrics and log case growth over time in Andorra. Credit: Alex Berke

These findings provide support for policies which aim to reduce transmission by encouraging people to remain distributed in their own home communities, thereby reducing both inter-community transmission and interaction potential. Furthermore, the events which draw the highest density of crowds should be identified and discouraged.

A pre-print article (Doorley et al., 2021) based on this work can be found at https://www.medrxiv.org/content/10.1101/2021.02.18.21251977v2

Learn more about the collaboration with Andorra here.

References

Dong, E., Du, H., & Gardner, L. (2020). An interactive web-based dashboard to track COVID-19 in real time. The Lancet infectious diseases, 20(5), 533–534.

Doorley, R.M., Berke, A., Noyman, A., Alonso, L.A., Ribo, J., Arroyo, V., Pons, M. & Larson, K. (2021). Mobility and COVID-19 in Andorra: Country-scale analysis of high-resolution mobility patterns and infection spread. medRxiv.

Royo-Cebrecos, C., Vilanova, D., López, J., Arroyo, V., Francisco, G., Pons, M., Carrasco, M.G., Piqué, J.M., Sanz, S., Dobaño, C. & García-Basteiro, A. (2020). Mass SARS-CoV-2 serological screening for the Principality of Andorra. Preprint: https://www.researchsquare.com/article/rs-119323/v1.

Uber (2020). Overview of the H3 Geospatial Indexing System. https://h3geo.org/docs/core-library/overview. Accessed: 2020–12–22

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